Critical assessment of proteome-wide label-free absolute abundance estimation strategies

Abstract

There is a great interest in reliable ways to obtain absolute protein abundances at a proteome-wide scale. To this end, label-free LC-MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low-cost and easily implemented quantification methods (Absolute Protein Expression, Exponentially Modified Protein Abundance Index, Intensity-Based Absolute Quantification Index, Top3, and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; that is, using spiked in stable isotope labeled standard peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of miscleaved, modified, and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy.

Robust Label-free, Quantitative Profiling of Circulating Plasma Microparticle (MP) Associated Proteins

“…quantification than the LFQ values, which is in agreement with observations by Ahrné and colleagues (24).…”

Quantitative analysis of human centrosome architecture by targeted proteomics and fluorescence imaging

“…Figure 3 and Table EV2 summarize the results for 73 core centrosomal proteins [including 58/61 proteins described in Azimzadeh et al (2012)]. The results obtained by iBAQ and SRM were generally in excellent agreement (Fig EV1E), confirming that label‐free quantification provides useful, albeit approximate, information about protein abundance (Ahrne et al , 2013). …”

“…To obtain quantitative information about a broader range of centrosomal proteins, we subjected the above centrosome preparations to shotgun proteomics and estimated protein abundance using the iBAQ method (Schwanhausser et al , 2011; Arike et al , 2012; Ahrne et al , 2013). Figure 3 and Table EV2 summarize the results for 73 core centrosomal proteins [including 58/61 proteins described in Azimzadeh et al (2012)].…”

High-Throughput LC–MS/MS Proteomic Analysis of a Mouse Model of Mesiotemporal Lobe Epilepsy Predicts Microglial Activation Underlying Disease Development

“…It has been suggested that top three approaches based on MS1 peak intensity are directly proportional to protein abundance and are associated with fewer errors than SC 24 . Since apparently each of these quantification approaches, which rely on different characteristics of the mass spectra to estimate peptide amounts, has certain advantages and limitations, their combined use should increase the reliability of identified protein changes 21,23 .…”